My Experience with AI-Powered API Testing using Keploy & Chrome Extension
From Manual Testing to AI: The Journey
As a developer, one of the most time-consuming and often overlooked phases of the development lifecycle is writing exhaustive test cases for every API endpoint. Whether it’s unit tests for business logic or integration tests for backend and database interaction—manual testing is slow, error-prone, and can become a bottleneck.
But recently, I had the chance to use Keploy—an AI-powered testing platform that helped me go from zero to high test coverage in minutes, without writing a single test case manually.
🛠️ What I Tested
I applied Keploy to my Task Manager Flask API project, which includes the following features:
RESTful API for creating, updating, deleting, and retrieving tasks.
MongoDB integration for data persistence.
Frontend interface built with HTML/CSS/JavaScript.
Swagger/OpenAPI schema auto-generated with flask-smorest.
🔁 Keploy API Testing — The Game Changer
Keploy allowed me to:
Record live API traffic as I used my app.
Generate real unit and integration tests automatically.
Replay those requests and compare responses to catch regressions.
I simply ran the server through Keploy’s CLI, used my frontend normally, and Keploy captured every request-response pair as test cases.
In just a few minutes, I had over 70%+ code coverage—without any manual test-writing.
Here's a snapshot from my Keploy dashboard:
🌐 API Testing with Chrome Extension
To complement my AI-based backend testing, I also explored Keploy’s Chrome extension to test real-world APIs on websites I frequently visit.
🔍 What I Tested:
GitHub API – By opening the GitHub homepage, I captured XHR requests like fetching user repos and notifications.
OpenWeatherMap API – I interacted with a weather site and intercepted the real-time weather fetch APIs.
The extension made it super easy to:
View API call details
Replay API requests instantly
Analyze responses directly from the browser
This gave me a deeper understanding of how modern SPAs (Single Page Applications) rely heavily on APIs under the hood.
✍️ Final Thoughts
Transitioning from manual API testing to AI-generated test suites was a game-changer for my workflow.
✨ Benefits I Experienced:
Zero boilerplate testing code.
Confidence in deployment due to automated regression checks.
Seamless CI/CD integration with GitHub Actions.
Keploy didn’t just save time—it helped me test smarter.
💬 What’s Next?
I plan to continue using Keploy for:
Future Flask and Django APIs.
Automated regression testing in CI pipelines.
Contributing to open source with high-quality, tested APIs.
If you're tired of writing boilerplate test cases, give Keploy a try.
The future of API testing is here—and it's AI-driven.
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